



import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple

import torch
from audiocraft.models import MusicGen

from transformers import (
    AutoFeatureExtractor,
    AutoTokenizer,
    EncodecModel,
    MusicgenDecoderConfig,
    MusicgenForConditionalGeneration,
    MusicgenProcessor,
    TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase			  =			logging.get_logger(__name__)


__UpperCAmelCase			  =			['''model.decoder.embed_positions.weights''']
def 						UpperCamelCase      (  snake_case__			:       int      )      ->	Tuple:
   if "emb" in name:
      UpperCamelCase       :    Any		      =      name.replace('emb'			,	'model.decoder.embed_tokens'      )
   if "transformer" in name:
      UpperCamelCase       :    Optional[Any]		      =      name.replace('transformer'			,	'model.decoder'      )
   if "cross_attention" in name:
      UpperCamelCase       :    Dict		      =      name.replace('cross_attention'			,	'encoder_attn'      )
   if "linear1" in name:
      UpperCamelCase       :    Optional[Any]		      =      name.replace('linear1'			,	'fc1'      )
   if "linear2" in name:
      UpperCamelCase       :    List[Any]		      =      name.replace('linear2'			,	'fc2'      )
   if "norm1" in name:
      UpperCamelCase       :    Any		      =      name.replace('norm1'			,	'self_attn_layer_norm'      )
   if "norm_cross" in name:
      UpperCamelCase       :    List[str]		      =      name.replace('norm_cross'			,	'encoder_attn_layer_norm'      )
   if "norm2" in name:
      UpperCamelCase       :    int		      =      name.replace('norm2'			,	'final_layer_norm'      )
   if "out_norm" in name:
      UpperCamelCase       :    Dict		      =      name.replace('out_norm'			,	'model.decoder.layer_norm'      )
   if "linears" in name:
      UpperCamelCase       :    List[Any]		      =      name.replace('linears'			,	'lm_heads'      )
   if "condition_provider.conditioners.description.output_proj" in name:
      UpperCamelCase       :    List[str]		      =      name.replace('condition_provider.conditioners.description.output_proj'			,	'enc_to_dec_proj'      )
   return name
def 						UpperCamelCase      (  snake_case__			:       OrderedDict			,	snake_case__			:       int      )      ->	Tuple[Dict, Dict]:
   UpperCamelCase       :    int		      =      list(state_dict.keys()      )
   UpperCamelCase       :    Dict		      =      {}
   for key in keys:
      UpperCamelCase       :    List[str]		      =      state_dict.pop(snake_case__      )
      UpperCamelCase       :    str		      =      rename_keys(snake_case__      )
      if "in_proj_weight" in key:
         # split fused qkv proj
         UpperCamelCase       :    Any		      =      val[:hidden_size, :]
         UpperCamelCase       :    Tuple		      =      val[hidden_size : 2 * hidden_size, :]
         UpperCamelCase       :    Optional[int]		      =      val[-hidden_size:, :]
      elif "enc_to_dec_proj" in key:
         UpperCamelCase       :    Union[str, Any]		      =      val
      else:
         UpperCamelCase       :    Optional[Any]		      =      val
   return state_dict, enc_dec_proj_state_dict
def 						UpperCamelCase      (  snake_case__			:       str      )      ->	MusicgenDecoderConfig:
   if checkpoint == "small":
      # default config values
      UpperCamelCase       :    Tuple		      =      1024
      UpperCamelCase       :    Union[str, Any]		      =      24
      UpperCamelCase       :    Any		      =      16
   elif checkpoint == "medium":
      UpperCamelCase       :    str		      =      1536
      UpperCamelCase       :    List[str]		      =      48
      UpperCamelCase       :    Optional[Any]		      =      24
   elif checkpoint == "large":
      UpperCamelCase       :    Tuple		      =      2048
      UpperCamelCase       :    Dict		      =      48
      UpperCamelCase       :    List[str]		      =      32
   else:
      raise ValueError(F"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."""      )
   UpperCamelCase       :    Union[str, Any]		      =      MusicgenDecoderConfig(
       hidden_size=snake_case__			,	ffn_dim=hidden_size * 4			,	num_hidden_layers=snake_case__			,	num_attention_heads=snake_case__			,	)
   return config
@torch.no_grad()
def 						UpperCamelCase      (  snake_case__			:       Optional[Any]			,	snake_case__			:       Dict=None			,	snake_case__			:       Tuple=None			,	snake_case__			:       Tuple="cpu"      )      ->	List[str]:
   UpperCamelCase       :    List[str]		      =      MusicGen.get_pretrained(snake_case__			,	device=snake_case__      )
   UpperCamelCase       :    Any		      =      decoder_config_from_checkpoint(snake_case__      )

   UpperCamelCase       :    int		      =      fairseq_model.lm.state_dict()
   UpperCamelCase      ,	UpperCamelCase       :    List[str]		      =      rename_state_dict(
       snake_case__			,	hidden_size=decoder_config.hidden_size      )

   UpperCamelCase       :    Optional[int]		      =      TaEncoderModel.from_pretrained('t5-base'      )
   UpperCamelCase       :    int		      =      EncodecModel.from_pretrained('facebook/encodec_32khz'      )
   UpperCamelCase       :    List[Any]		      =      MusicgenForCausalLM(snake_case__      ).eval()

   # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
   UpperCamelCase      ,	UpperCamelCase       :    Optional[int]		      =      decoder.load_state_dict(snake_case__			,	strict=snake_case__      )

   for key in missing_keys.copy():
      if key.startswith(('text_encoder', 'audio_encoder')      ) or key in EXPECTED_MISSING_KEYS:
         missing_keys.remove(snake_case__      )

   if len(snake_case__      ) > 0:
      raise ValueError(F"""Missing key(s) in state_dict: {missing_keys}"""      )

   if len(snake_case__      ) > 0:
      raise ValueError(F"""Unexpected key(s) in state_dict: {unexpected_keys}"""      )

   # init the composite model
   UpperCamelCase       :    int		      =      MusicgenForConditionalGeneration(text_encoder=snake_case__			,	audio_encoder=snake_case__			,	decoder=snake_case__      )

   # load the pre-trained enc-dec projection (from the decoder state dict)
   model.enc_to_dec_proj.load_state_dict(snake_case__      )

   # check we can do a forward pass
   UpperCamelCase       :    int		      =      torch.arange(0			,	8			,	dtype=torch.long      ).reshape(2			,	-1      )
   UpperCamelCase       :    str		      =      input_ids.reshape(2 * 4			,	-1      )

   with torch.no_grad():
      UpperCamelCase       :    Union[str, Any]		      =      model(input_ids=snake_case__			,	decoder_input_ids=snake_case__      ).logits

   if logits.shape != (8, 1, 2048):
      raise ValueError('Incorrect shape for logits'      )

   # now construct the processor
   UpperCamelCase       :    List[str]		      =      AutoTokenizer.from_pretrained('t5-base'      )
   UpperCamelCase       :    Any		      =      AutoFeatureExtractor.from_pretrained('facebook/encodec_32khz'			,	padding_side='left'      )

   UpperCamelCase       :    List[Any]		      =      MusicgenProcessor(feature_extractor=snake_case__			,	tokenizer=snake_case__      )

   # set the appropriate bos/pad token ids
   UpperCamelCase       :    Any		      =      2048
   UpperCamelCase       :    Any		      =      2048

   # set other default generation config params
   UpperCamelCase       :    Dict		      =      int(30 * audio_encoder.config.frame_rate      )
   UpperCamelCase       :    str		      =      True
   UpperCamelCase       :    int		      =      3.0

   if pytorch_dump_folder is not None:
      Path(snake_case__      ).mkdir(exist_ok=snake_case__      )
      logger.info(F"""Saving model {checkpoint} to {pytorch_dump_folder}"""      )
      model.save_pretrained(snake_case__      )
      processor.save_pretrained(snake_case__      )

   if repo_id:
      logger.info(F"""Pushing model {checkpoint} to {repo_id}"""      )
      model.push_to_hub(snake_case__      )
      processor.push_to_hub(snake_case__      )


if __name__ == "__main__":
    __UpperCAmelCase			  =			argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--checkpoint''',
        default='''small''',
        type=str,
        help='''Checkpoint size of the MusicGen model you\'d like to convert. Can be one of: `[\'small\', \'medium\', \'large\']`.''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder''',
        required=True,
        default=None,
        type=str,
        help='''Path to the output PyTorch model directory.''',
    )
    parser.add_argument(
        '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
    )
    parser.add_argument(
        '''--device''', default='''cpu''', type=str, help='''Torch device to run the conversion, either cpu or cuda.'''
    )

    __UpperCAmelCase			  =			parser.parse_args()
    convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)








import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


a      :  Optional[int]			     =			logging.getLogger(__name__)







@dataclass
class 				_a				(   _lowerCAmelCase					):
			A      		=	field(
			    default=0.0 ,		metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''Whether to SortishSamler or not.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''whether to use adafactor'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}					)
			A      		=	field(default=_lowerCAmelCase ,		metadata={'''help''': '''Dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default=_lowerCAmelCase ,		metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}					)
			A      		=	field(
			    default='''linear''' ,		metadata={'''help''': F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()					)}'''} ,		)






